Search Results (8,835)

Although beer fermentation has traditionally been carried out with Saccharomyces, the boom in craft brewing has led to the use of non-conventional yeast species for beer production. This group also includes non-Saccharomyces starters, which are commonly used in winemaking and which have different technological characteristics compared to standard representatives of the Saccharomyces genus. One of the important characteristics of the non-Saccharomyces group is the richer enzyme profile, which leads to the production of beverages with different taste and aroma profiles. The aim of this study was to investigate sweet and hopped wort fermentation with seven strains of active dry non-conventional yeasts of Lachancea spp., Metschnikowia spp., Torulaspora spp. and a mixed culture of Saccharomyces cerevisiae and Torulaspora delbrueckii. One ale and one lager active dry yeast strain were used as control strains. The extract consumption, ethanol production, degree of fermentation, pH drop, as well as the yeast secondary metabolites formed by the yeast (higher alcohols, esters and aldehydes) in sweet and hopped wort were investigated. The results indicated that all of the studied types of non-conventional yeasts have serious potential for use in beer production in order to obtain new beer styles. For the purposes of this study, statistical methods, principle component analysis (PCA) and correlation analysis were used, thus establishing the difference in the fermentation kinetics of the growth in the studied species in sweet and hopped wort. It was found that hopping had a significant influence on the fermentation kinetics of some of the species, which was probably due to the inhibitory effect of the iso-alpha-acids of hops. Directions for future research with the studied yeast species in beer production are presented. Full article

Sodium chloride is a chemical compound that has been encountered by people for thousands of years, and plays a significant role in their lives. The aim of this article is to provide a comprehensive review of table salt from the perspective of health, food technology, and cultural heritage. The article discusses salt extraction and production, its composition and consumption, and its effects on the human body. The authors draw attention to new trends, such as the use of micronized salt, microencapsulated salt, and salt with colors and shapes that differ from those of typical table salt. Scientific studies on the presence of undesirable substances and the use of salt additives were reviewed. The role of salt in dairy, meat, and bakery technology was illustrated. Gaps in research on salt were highlighted. In the last part, all types of salt with geographical indications are shown. The paper suggests that producers with a long tradition in the salt sector should apply for the European geographical indications to enhance their national and cultural heritage and promote their region. The review highlights the need for further research on all aspects discussed. Full article

In line with the goals of “peak carbon emissions and carbon neutrality”, this study aims to develop a market-coordinated operation mechanism to promote renewable energy adoption and consumption, addressing the challenges of integrating medium- and long-term trading with spot markets in power systems with high renewable energy penetration. A three-stage joint operation framework is proposed. First, a medium- and long-term trading game model is established, considering multiple energy types to optimize the benefits of market participants. Second, machine learning algorithms are employed to predict renewable energy output, and a contract decomposition mechanism is developed to ensure a smooth transition from medium- and long-term contracts to real-time market operations. Finally, a day-ahead market-clearing strategy and an incentive-compatible settlement mechanism, incorporating the constraints from contract decomposition, are proposed to link the two markets effectively. Simulation results demonstrate that the proposed mechanism effectively enhances resource allocation and stabilizes market operations, leading to significant revenue improvements across various generation units and increased renewable energy utilization. Specifically, thermal power units achieve a 19.12% increase in revenue, while wind and photovoltaic units show more substantial gains of 38.76% and 47.52%, respectively. Concurrently, the mechanism drives a 10.61% increase in renewable energy absorption capacity and yields a 13.47% improvement in Tradable Green Certificate (TGC) utilization efficiency, confirming its overall effectiveness. This research shows that coordinated optimization between medium- and long-term/spot markets, combined with a well-designed settlement mechanism, significantly strengthens the market competitiveness of renewable energy, providing theoretical support for the market-based operation of the new power system. Full article

Goji berries (Lycium barbarum L.) are considered a functional food due to their high content of bioactive compounds with demonstrated health benefits. This study evaluated four cultivars (G3, G4, G5, and G7) grown under Mediterranean climate conditions, focusing on their physicochemical properties (total soluble solids, titratable acidity, and pH), bioactive compound (sugars and organic acids, total and individual phenolic and carotenoid compounds, and antioxidant activities (DPPH and CUPRAC assay)), and aromatic profiles (by GC-MS) to assess their suitability for fresh consumption or incorporation into food products. G4 exhibited the most favorable physicochemical characteristics, with the highest total soluble solids (20.2 °Brix) and sugar content (92.8 g 100 g⁻¹ dw). G5 stood out for its lower titratable acidity (0.34%) and highest ripening index (54.8), indicating desirable flavor attributes. Concerning bioactive compounds, G3 and G4 showed the highest total phenolic content (17.9 and 19.1 mg GAE g⁻¹ dw, respectively), with neochlorogenic acid being predominant. G4 was notable for its high carotenoid content, particularly zeaxanthin (1722.6 μg g⁻¹ dw). These compounds significantly contributed to antioxidant activity. Volatile organic compound (VOC) profiles revealed alcohols and aldehydes as the dominant chemical families, with hexanal being the most abundant. G5 and G7 exhibited the highest total VOC concentrations. Principal component analysis grouped G3 and G4 based on their high sugar and phenolic content, while G5 and G7 were characterized by their complex aromatic profiles. Therefore, G3 and G4 are promising candidates for fresh consumption and potential functional applications, while G5 and G7 are particularly suitable for new product development due to their nutraceutical and aromatic value. Full article

The widespread adoption of electric buses represents a major step forward in sustainable transportation, but also brings new operational challenges, particularly in terms of improving their efficiency and controlling costs. Therefore, battery energy consumption management is a key approach for addressing these issues. Accurate prediction of energy consumption and interpretation of the influencing factors are essential for improving operational efficiency, optimizing energy use, and reducing operating costs. Although existing studies have made progress in battery energy consumption prediction, challenges remain in achieving high-precision modeling and conducting a comprehensive analysis of the influencing features. To address these gaps, this study proposes a two-layer stacking framework for estimating the energy consumption of electric buses. The first layer integrates the strengths of three nonlinear regression models—RF (Random Forest), GBDT (Gradient Boosted Decision Trees), and CatBoost (Categorical Boosting)—to enhance the modeling capacity for complex feature relationships. The second layer employs a Linear Regression model as a meta-learner to aggregate the predictions from the base models and improve the overall predictive performance. The framework is trained on 2023 operational data from two electric bus routes (NO. 355 and NO. W188) in Changsha, China, incorporating battery system parameters, driving characteristics, and environmental variables as independent variables for model training and analysis. Comparative experiments with various ensemble models demonstrate that the proposed stacking framework exhibits superior performance in data fitting. Furthermore, XGBoost (Extreme Gradient Boosting) is introduced as a surrogate model to approximate the decision logic of the stacking framework, enabling SHAP (SHapley Additive exPlanations) analysis to quantify the contribution and marginal effects of influencing features. The proposed stacked and surrogate models achieved superior battery energy consumption prediction accuracy (lowest MSE, RMSE, and MAE), significantly outperforming benchmark models on real-world datasets. SHAP analysis quantified the overall contributions of feature categories (battery operation parameters: 56.5%; driving characteristics: 42.3%; environmental data: 1.2%), further revealing the specific contributions and nonlinear influence mechanisms of individual features. These quantitative findings offer specific guidance for optimizing battery system control and driving behavior. Full article

Governments worldwide have set ambitious targets for decarbonising energy grids, driving the need for increased renewable energy generation and improved energy efficiency. One key strategy for achieving this involves enhanced energy management in buildings, often using machine learning-based forecasting methods. However, such methods typically rely on extensive historical data collected via costly sensor installations—resources that many buildings lack. This study introduces a novel forecasting approach that eliminates the need for large-scale historical datasets or expensive sensors. By integrating custom-built models with existing energy data, the method applies calculated weighting through a distance matrix and accuracy coefficients to generate reliable forecasts. It uses readily available building attributes—such as floor area and functional type to position a new building within the matrix of existing data. A Euclidian distance matrix, akin to a K-nearest neighbour algorithm, determines the appropriate neural network(s) to utilise. These findings are benchmarked against a consolidated, more sophisticated neural network and a long short-term memory neural network. The dataset has hourly granularity over a 24 h horizon. The model consists of five bespoke neural networks, demonstrating the superiority of other models with a 610 s training duration, uses 500 kB of storage, achieves an R² of 0.9, and attains an average forecasting accuracy of 85.12% in predicting the energy consumption of the five buildings studied. This approach not only contributes to the specific goal of a fully decarbonized energy grid by 2050 but also establishes a robust and efficient methodology for maintaining standards with existing benchmarks while providing more control over the method. Full article

The accurate and precise measurement of lubricating oil consumption is critical for developing environmentally friendly internal combustion engines, particularly hydrogen-fueled internal combustion engines. The deuterium tracer method is based on the addition of poly-deuterated base oil tracers to fully formulated oils for precise, accurate, and fast lubricating oil consumption measurements. Previously performed measurements have shown that the use of poly-deuterated poly-alpha olefins has minimal impact on lubricating oil properties, except for a slight drop in oil viscosity. To further reduce the impact on lubricating oil characteristics, a new base oil for the synthesis of a poly-deuterated tracer is introduced, and its influence on the lubricating oil’s chemical, tribological, and rheological properties is analyzed. Furthermore, the influence of the tracer addition on the preignition tendencies of the fully formulated oil is also examined. Based on the analyses, no relevant changes in the lubricating oil properties, such as viscosity, density, and thermal degradation behavior, can be observed. Additionally, the deuterium tracer does not negatively influence combustion anomalies, thus reducing preignition tendencies. These results establish the method’s compatibility with new-generation engines, especially hydrogen-fueled internal combustion engines. Full article

The restrictions on business and social activity during the COVID-19 pandemic have led to significant changes in consumption patterns worldwide. Such changes are causing structural shifts in the markets of goods and services, thus affecting regional resilience. In this article, we aim to assess the changing structure of the consumption of goods and services in highly urbanized Russian regions under the impact of the COVID-19 pandemic and to analyze its effects on the lifestyle of the population. According to our results, some Russian regions demonstrate a return to previous consumption levels, while others exhibit the emergence of new dynamics. The conclusion is made that COVID restrictions have invoked a paradigm shift in consumer behavior toward investment in self-development, safety, and comfort. This observation should be taken into account when developing strategies for the recovery growth of regional economies. Full article

This paper discusses the integrated technology of CO₂ adsorption and catalysis, which combines adsorption and catalytic conversion, simplifies the traditional process, reduces energy consumption, and improves efficiency. The traditional carbon capture technology has the problems of high energy consumption, equipment corrosion, and absorbent loss, while the integrated technology realizes the adsorption, conversion, and catalyst regeneration of CO₂ in a single reaction system, avoiding complex desorption steps. Through micropore confinement and surface electron transfer mechanism, the technology improves the reactant concentration and mass transfer efficiency, reduces the activation energy, and realizes the low-temperature and high-efficiency conversion of CO₂. In terms of materials, MOF-based composites, alkali metal modified oxides, and carbon-based hybrid materials show excellent performance, helping to efficiently adsorb and transform CO₂. However, the design and engineering of reactors still face challenges, such as the development of new moving bed reactors. This technology provides a new idea for CO₂ capture and resource utilization and has important environmental significance and broad application prospects. Full article

Siniperca chuatsi is an important high-quality freshwater aquaculture species in China. In nature, it feeds exclusively on live food. In this study, domesticated juvenile S. chuatsi were divided into three groups and fed live food (group L), compound feed (group C), or a mixed diet (group M) for three months to investigate the molecular mechanisms underlying adaptation to compound feed. Histopathological examination revealed that compound feed consumption induced looser liver cell arrangement, hepatocyte morphological irregularities, and vacuolization. A total of 1033 and 1428 differentially expressed genes (DEGs), and 187 and 184 differential metabolites (DMs), were identified in the C vs. L and C vs. M groups, respectively. Transcriptomic analysis revealed that the significantly and commonly enriched metabolic pathways shared by both comparison groups were predominantly involved in amino acid, carbohydrate, and lipid metabolisms. Metabolomic analysis demonstrated that the significantly and commonly enriched metabolic pathways shared by both comparison groups were the arachidonic acid metabolism, linoleic acid metabolism, oxidative phosphorylation, and PPAR signalling pathways. Integrated omics analysis showed that the PPAR signalling pathway was the only significantly co-enriched pathway across both omics datasets. This study provides new insights into the molecular mechanisms of compound feed adaptation and provides theoretical support for selecting feed traits in S. chuatsi. Full article

The green transformation of traditional energy structures and the development of the new energy industry are crucial drivers of sustainable development in the country. The ASEAN Plan of Action for Energy Cooperation (2016–2025; APAEC [2016–2025]), established in 2016, has significantly promoted the growth of the new energy sector and enhanced energy structures across Association of Southeast Asian Nations (ASEAN). This initiative has also inspired these countries to develop corresponding industrial policies aimed at supporting the new energy vehicle (NEV) industry, resulting in significant growth in this sector within the ASEAN region. This paper analyzes the factors influencing the development of the NEV industry in the context of ASEAN’s new energy policies, drawing empirical insights from data collected across six ASEAN countries from 2013 to 2024. Following the implementation of the APAEC (2016–2025), it was observed that ASEAN countries reached a consensus on energy development and cooperation, collaboratively advancing the NEV industry through regional policies. Furthermore, factors such as national governance, financial development, education levels, and the size of the automotive market positively contribute to the growth of the NEV industry in ASEAN. Conversely, high energy consumption can hinder its progress. Additionally, further research indicates that the APAEC (2016–2025) has exerted a more pronounced impact on countries with robust automotive industry foundations or those prioritizing relevant policies. The findings of this paper offer valuable insights for ASEAN countries in the formulating policies for the NEV industry, optimizing energy structures, and achieving low-carbon energy transition and sustainable development. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder marked by amyloid-β (Aβ) plaques, neurofibrillary tangles, blood–brain barrier dysfunction, oxidative stress (OS), and neuroinflammation. Current treatments provide symptomatic relief, but do not halt the disease’s progression. OS plays a crucial role in AD pathogenesis by promoting Aβ accumulation. Nuclear factor erythroid 2-related factor 2 (NRF2) is a key regulator of the antioxidant response, influencing genes involved in OS mitigation, mitochondrial function, and inflammation. Dysregulation of NRF2 is implicated in AD, making it a promising therapeutic target. Emerging evidence suggests that adherence to a Mediterranean diet (MD), which is particularly rich in polyphenols from extra virgin olive oil (EVOO), is associated with improved cognitive function and a reduced risk of mild cognitive impairment. Polyphenols can activate NRF2, enhancing endogenous antioxidant defenses. This study employs a computational approach to explore the potential of bioactive compounds in EVOO to modulate NRF2-related pathways in AD. We analyzed transcriptomic data from AD and EVOO-treated samples to identify NRF2-associated genes, and used chemical structure-based analysis to compare EVOO’s bioactive compounds with known NRF2 activators. Enrichment analysis was performed to identify common biological functions between NRF2-, EVOO-, and AD-related pathways. Our findings highlight important factors and biological functions that provide new insight into the molecular mechanisms through which EVOO consumption might influence cellular pathways associated with AD via modulation of the NRF2 pathway. The presented approach provides a different perspective in the discovery of compounds that may contribute to neuroprotective mechanisms in the context of AD. Full article

Understanding vehicle emissions is essential for developing effective carbon reduction strategies in the transport sector. Conventional emission models often assume homogeneity and linearity, overlooking real-world asymmetries that arise from variations in vehicle design and powertrain configurations. This study explores how machine learning and explainable AI techniques can effectively capture both symmetric and asymmetric emission patterns across different vehicle types, thereby contributing to more sustainable transport planning. Addressing a key gap in the existing literature, the study poses the following question: how do structural and behavioral factors contribute to asymmetric emission responses in internal combustion engine vehicles compared to new energy vehicles? Utilizing a large-scale Spanish vehicle registration dataset, the analysis classifies vehicles by powertrain type and applies five supervised learning algorithms to predict CO₂ emissions. SHapley Additive exPlanations (SHAPs) are employed to identify nonlinear and threshold-based relationships between emissions and vehicle characteristics such as fuel consumption, weight, and height. Among the models tested, the Random Forest algorithm achieves the highest predictive accuracy. The findings reveal critical asymmetries in emission behavior, particularly among hybrid vehicles, which challenge the assumption of uniform policy applicability. This study provides both methodological innovation and practical insights for symmetry-aware emission modeling, offering support for more targeted eco-design and policy decisions that align with long-term sustainability goals. Full article

Achieving synergies between government-led and market-based policy instruments is critical to advancing Energy–Environmental Productivity and Sustainable Urban Development. This study investigates the effects of China’s dual-pilot energy policies (New Energy Demonstration Cities (NEDCs) and Energy Consumption Permit Trading (ECPT)) on urban environmental productivity (UEP) across 279 prefecture-level cities from 2006 to 2023. Utilizing a Non-Radial Directional Distance Function (NDDF) approach, combined with Difference-in-Differences (DID) estimation and spatial econometric models, the analysis reveals that these synergistic policies significantly enhance both comprehensive and net measures of UEP. Mechanism analysis highlights the roles of industrial restructuring, technological innovation, and energy transition in driving these improvements, while heterogeneity analysis indicates varying effects across different city types. Spatial spillover analysis further demonstrates that policy impacts extend beyond targeted cities, contributing to broader regional gains in UEP. These findings offer important insights for the design of integrated energy and environmental policies and support progress toward key Sustainable Development Goals (SDG 7, SDG 11, and SDG 12). Full article

Potentially toxic elements, such as chromium (Cr) and zinc (Zn), play essential roles in humans and animals. However, the harmful effects of excessive exposure to these elements through food remain unknown. In this sense, this study aimed to evaluate the anthropogenic contamination of chromium and zinc in aquatic biota and seafood consumers. Based on the PRISMA protocol, 67 articles were selected for this systematic review. The main results point to a wide distribution of these elements, which have familiar emission sources in the aquatic environment, especially in highly industrialized regions. Significant concentrations of both have been reported in different fish species, which sometimes represent a non-carcinogenic risk to consumer health and a carcinogenic risk related to Cr exposure. New studies should be encouraged to fill gaps, such as the characterization of the toxicity of these essential elements through fish consumption, determination of limit concentrations updated by international regulatory institutions, especially for zinc, studies on the influence of abiotic factors on the toxicity and bioavailability of elements in the environment, and those that evaluate the bioaccessibility of these elements in a simulated digestion system when in high concentrations. Full article